Home > Microbiology > Determination of Effects of Genetic Diversity of Oenococcus oeni and Physicochemical Characteristics on Malolactic Fermentation Across Chilean Vineyards, using Multivariate Methods

Determination of Effects of Genetic Diversity of Oenococcus oeni and Physicochemical Characteristics on Malolactic Fermentation Across Chilean Vineyards, using Multivariate Methods

ABSTRACT

After alcoholic fermentation, Oenococcus oeni is the protagonist that carry out malolactic fermentation in order to improve the quality of wine. However, the effectiveness of malolactic fermentation varies according to intraspecific diversity of O. oeni at a genetic level and physicochemical concentrations of intrinsic factors. In this study, we assessed both components by isolating O. oeni from three Chilean vineyards (Maipo, Colchagua y Curicó) and measuring intrinsic components, such as biogenic amines and amino acids, during malolactic fermentation. Both data were condensed and then processed with Multivariate using the statistical method of correspondence analysis. The genetic data was clustered using the RAPD-PCR molecular method, and the physicochemical analysis was carried out using chromatography techniques. Isolation of O. oeni species and genetic diversity results between the vineyards revealed that O. oeni strains cluster according to its geographical origin, with strains similarity higher than 60% of all the samples. The wines of each valley could be characterized by the presence or absence of biogenic amines, and final correspondence analysis showed that there is a differentiation of strains between the studied Valleys, also confirming the accomplishment of the malolactic fermentation in the wines analyzed.

INTRODUCTION

Wine is produced by natural alcoholic fermentation of must, which is mostly performed by Saccharomyces yeasts1,2. After alcoholic fermentation, a second fermentation called malolactic fermentation is conducted to improve intensity of flavors and increase microbial stability in the wine2. Malolactic fermentation is carried out by lactic acid bacteria, being Oenococcus oeni the main species in charge of the process3–7. O. oeni is gram-positive bacteria that is able to resist low pH (lower than 3.5), high levels of SO2 (50 mg/L) and ethanol concentration (greater than 10.5 v/v)8. Although O. oeni has good adaptation to wine environment, it has shown phenotypic and genotypic diversity at strain level9 that may have an effect on the result of malolactic fermentation and consequently, it produces inconsistencies in the quality of the wine. This has led to the selection of resistant strains from different environments6, in which intraspecific O. oeni biodiversity studies have been assessed comparing RAPD-PCR results and it has showed a large diversity and correlation between strains and geographical origin10–12. These observations give insights of the potential of identifying O. oeni strains from different geographical regions for the selection of resistant strains and development of fermentation starters that will improve the oenological properties of local wines.

Although malolactic fermentation enhances the wine microbial stability and flavor, the process can be risky because some biogenic amines may increase their levels generating the opposite effect: undesired aroma and flavor13,14. The increased of the concentration of biogenic amines has been attributed to the action of lactic acid bacteria that decarboxylates the amino acids present in the wine, which can also be influenced by intrinsic features such as amino acids concentration, sugars, ethanol concentration, etc.

The aim of this study was to evaluate the effect of genetic diversity on the consumption of amino acids and the production of biogenic amines. For this, we evaluate isolates of O. oenipopulation genetic clustering obtained of three Central Valleys of Chile: Maipo, Curicó y Colchagua and correlate them with malolactic fermentation, biogenic amines and amino acids concentrations from vintages from the same vineyards using correspondence analysis and Partial Least Square Discriminant Analysis (PLS-DA).

Samples were labeled as M[A/B/C]-X, which corresponds to molecular information from zone A: Maipo, zone B: Colchagua and zone C: Curicó, and X the isolated strain number. Then, the isolates identified as O. oeni a malolactic region of 1025 bp was amplified with PCR using primers On1 (5’-TAA TGT GGT TCT TGA GGA GAA AAT-3’) and On2 (5’-ATC ATC GTC AAA CAA GAG GCC TT-3’ )17. PCR amplification was carried out during 30 cycles of amplification with denaturation during 45 s at 95°C, annealing for 2 min at 64°C, extension of 2 min at 72°C. PCR products were visualized in 1% agarose gel electrophoresis. Finally, O. oeni strains diversity was evaluated using short primer as previously described RAPD technique10. PCR program was repeated during 30 cycles of amplification with denaturation during 1 min at 94°C, annealing for 2 min at 40°C, extension of 2 min at 72°C, and final 10 min extension at 72°C. Products were visualized in 1.4% agarose gel. All the electrophoresis gels were staining of 5 µg ethidium bromide per L. Electropherograms were processed using the software Quantity One version 4.1.1 (Bio-Rad, USA) to calculate distances employing UPGMA cluster analysis, in order to build a dendrogram with the tool Free Tree versión 0.9.1.5018 and visualized it in Figtree v1.4.2.

Biogenic amines characterization during malolactic fermentationWines samples collected during malolactic fermentation were chemically analyzed for: a)Biogenic amines quantification (histamine, tyramine, putrescine, cadaverine and phenylethylamine) from samples collected during malolactic fermentation. For this, was carried out using reversed phase HPLC method, according to previously reported conditions14. Biogenic amines are determined by derivatization using mainly benzoyl chloride and posterior extraction with chloroform. Final residue was resuspended in acetonitrile/water mixture for HPLC analysis. Chromatography separation was carried out at a constant temperature of 22°C using a column of 150 x 4,6 nm of dimensions and 5 µ particle size (LUNA, Phenomenex, USA). b) Amino acids quantification was carried out using reversed phase HPLC method19, the samples were derivatized with phenyl isocyanate in order to form phenylthiocarbamide amino acids, which were separated and quantified through gradient chromatography at a constant temperature of 45 ºC. The mobile phase was composed of a solution (A) of sodium acetate anhydrous (0.14 M, pH 6.4) and acetonitrile (proportion 96:6). And a solution (B) composed of water and acetonitrile (proportion 60:40).

Correspondence analysis between genetic diversity and biogenic amines contentThe genetic diversity of O. oeni and the measurements of intrinsic physicochemical parameters of the wine are two independent variables that were collected in the same vineyards. For this reason, both data were compared using the statistical method of correspondence analysis (CA), which aims to compare qualitative data into factorial analysis of both variables. CA is a multidimensional method to conforms homogenous groups from eventual similarities (or differences) of the isolates map indicating relative distance among isolates20. Results of CA were submitted to condensation/reduction of the dimensionality using the method of Karhunen–Loeve21. Following a PLS-DA were performed.

PLS-DA is an asymmetric method to predict one data set separate from another while compute two sets differently. Indeed, PLS-DA is a linear regression method whereby the multivariate variables corresponding to the observations (physicochemical markers) are related to the class membership for each sample. This modeling technique establishes the relationship between two sets of predictor and response variables. It is a correlation analysis that estimates the values of one variable from a set of controllable independent variables14.

The result was validated by full cross-validation routines, minimizing the prediction residual sum of squares function (PRESS) to avoid overfitting the models22. All analyses were performed using the SPSS 19 (IBM SPSS, USA, 2016) and SIMCA P+ 12 (Umetrics AB, Sweden, 2012) software.

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